Experimental and 3D-ANN based analysis and prediction of cutting forces, tool vibration and tool wear in boring of Ti-6Al-4V alloy

Abstract

In this work, accurate 3D finite element models were developed to study and predict machining characteristics like tool vibration, tool wear, surface roughness, cutting force and thrust forces in the boring of Ti-6Al-4V alloy. Experiments were conducted on the proposed metal using carbide inserts at three levels of spindle speeds, depth of cuts and feed rates and experimental results were collected. Numerical simulation was carried out using Deform 3D software. Johnson-cook material model was also used in simulation to predict the machining characteristics. A Usui's wear model was taken in simulation to calculate tool wear at different working conditions. Experimental data of the five machining characteristics were analysed using analysis of variance to identify the most significant parameters. Cutting speed, depth of cut and feed rate were found to be the most significant parameters. Simulated results of the machining characteristics were compared with the experimental data and found in a good agreement between them. An Artificial neural network (ANN) model was also developed and trained with the experimental data to validate the results. FEM simulation models provide relevant machining information without conducting experimentation for any metal.